• Support from NICHD, NIH/OD, NIMH, & NIDA via R01HD094830-01; NSF via 2032713; the LEGO Foundation; & the Alfred P. Sloan Foundation
• Karen Adolph, Cathie Tamis-LeMonda, Orit Hertzberg, Tiger Teng

• A cautionary tale
• (Hyper)active Curation
• Lessons learned

(NYU Health Sciences Library, 2013)

• Embed data curation within data collection workflow
• Rigorous & thorough quality assurance (QA) during collection
• Curate data with specific sharing target in mind

• “Heavy” vs. “light” QA
• Semi-automated QA reports from Databrary
  • https://github.com/PLAY-behaviorome/workflow/tree/master/session_qa_reports
  • Not public

• https://github.com/PLAY-behaviorome/KoBoToolbox
• Series of enumerated R Markdown documents
  • Reproducible
  • Documents gathering, cleaning steps

• Final CSV uploaded to Databrary
• play_data <- databraryapi::read_csv_data_as_df(session_id = 51539, asset_id = 366382)

• Full protocol on https://play-project.org
• Considering migration of protocol to bookdown
• Individual-level files
  • Including CHAT export of transcripts
• Clean, aggregate MB-CDI data
• From data dictionaries to open schemata

“…psychologists tend to treat other peoples’ theories like toothbrushes; no self-respecting individual wants to use anyone else’s.”

(Mischel, 2009)

“The toothbrush culture undermines the building of a genuinely cumulative science, encouraging more parallel play and solo game playing, rather than building on each other’s directly relevant best work.”

(Mischel, 2009)

“…psychologists tend to treat other peoples’ theories data, data management practices, tasks, displays…like toothbrushes…”

Cheeky open and reproducible developmental science advocates who want to create a more cumulative science

• You have to curate data for yourself, so…
• Clear to you == (often) clear to others
• Curate with specific target in mind

• Automate as much as possible
  • Script, use APIs
  • Exploit the web
  • Consistency is the hobgoblin of little minds key to successful automation!

• Test workflow at every step/look at data
• Expect to iterate
• Don’t make the perfect the enemy of the good
• Meaningful data management plans increasingly required by funders
• Make data a first class product

• Share best practices
• Talk about “you know”
• Let’s solve as-yet-unsolved problems…together
• No reinventing wheels

This talk was produced on 2022-04-25 in RStudio using R Markdown and the ioslides framework. The code and materials used to generate the slides may be found at https://github.com/PLAY-behaviorome/2022-04-21-team-sci-cds/. Information about the R Session that produced the code is as follows:

## R version 4.1.2 (2021-11-01)
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##  [7] backports_1.4.1    bslib_0.3.1        utf8_1.2.2        
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## [46] kableExtra_1.3.4   Matrix_1.3-4       Rcpp_1.0.8.3      
## [49] munsell_0.5.0      fansi_1.0.3        lifecycle_1.0.1   
## [52] stringi_1.7.6      likert_1.3.5       yaml_2.3.5        
## [55] plyr_1.8.6         grid_4.1.2         parallel_4.1.2    
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## [70] glue_1.6.2         evaluate_0.15      modelr_0.1.8      
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## [76] cellranger_1.1.0   gtable_0.3.0       assertthat_0.2.1  
## [79] xfun_0.30          ggExtra_0.9        mime_0.12         
## [82] xtable_1.8-4       broom_0.7.11       databraryapi_0.2.8
## [85] later_1.3.0        viridisLite_0.4.0  ellipsis_0.3.2